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In order to optimize the performance of the multi-verse algorithm to solve the optimal value of function, this paper proposed a global optimization multiverse algorithm（G-MVO） with an improved search mechanism. Aiming at the defects of the standard algorithm, which was easy to fall into local optimum and premature convergence due to the single search mechanism, this paper proposed three learning strategies to enhance the algorithm performance: through multi-strategy interaction and cooperation to reduce the complexity of the algorithm and improve the accuracy of the solution, designing adaptive parameters to dynamically choose the best strategy and optimizing the performance of the algorithm globally. In order to verify the effectiveness of the G-MVO algorithm, simulation experiments were carried out on 8 benchmark functions of different dimensions. Experimental results show that the proposed algorithm has better solution precision and convergence speed. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithm-SVM is a typical framework of multi-classification evaluation model, which has important theoretical and practical significance in multi-classification comprehensive decision-making. Therefore, this paper proposed a multi-classification evaluation algorithm of an improved whale optimization algorithm using Lévy flight and elite opposition-based learning SVM（LFEO-BWOA-SVM）. It used Lévy flight strategy instead of spiral trajectory strategy to update position information effectively, and could overcome the deficiency of whale optimization algorithm which was easy to fall into local optimization. It introduced the elite opposition-based learning mechanism to increase population diversity and improved the global optimization ability of whale optimization algorithm. The simulation results show that the classification accuracy of LFEO-BWOA-SVM algorithm is 17.84% and 4.51% higher than that of traditional SVM and BP neural network, the accuracy is 98.73%, and the training time is 9.34% and 84.94% shorter than that of standard WOA-SVM and PSO-SVM, respectively. The experimental results show that the optimization ability and convergence speed of LFEO-BWOA-SVM algorithm are obviously improved, and the accuracy and rapidity are good. [ABSTRACT FROM AUTHOR]

To solve the problem that the ant lion optimization algorithm was prone to local optimal stagnation, low convergence accuracy and slow convergence speed, this paper proposed the ant lion optimization algorithm integrating with Lévy flight and golden sinusoids ( LGSALO). This algorithm used the variation mechanism of Lévy flight to carry out mutation operation on the position update mode in the optimization process, which could improve the population diversity, make the algorithm jump out of the local optimum, improve the global optimization ability, and avoid the premature convergence of the algorithm to some extent. At the same time, the algorithm introduced the golden sine algorithm to improve the optimization mode of the elite ant lion and coordinate the ability of global exploration and local development. The experimental simulation results show that the improved algorithm has good performance of optimization and strong development ability. [ABSTRACT FROM AUTHOR]